What is the typical cost for a custom generative AI project?
Costs vary widely based on complexity. A rapid prototype or MVP can range from $25,000 to $75,000. A
full-scale, production-grade enterprise application typically starts at $150,000 and can go higher
depending on the scope. We provide a detailed, transparent quote after our initial discovery phase.
How do you ensure our data remains confidential?
Data security is our top priority. We achieve this primarily by deploying solutions within your own
infrastructure (private cloud or on-premise). Your data never leaves your control. For all projects, we
operate under strict NDAs and are SOC 2 and ISO 27001 certified, which audits our security controls.
What kind of results can we expect?
Results are tied to the specific use case. Common outcomes include: 30-70% reduction in manual effort for
a given workflow, 15-40% increase in conversion rates from personalization, 90%+ reduction in false
positives for compliance monitoring, and a 5x acceleration in content creation.
How long does it take to see an ROI?
For many automation and efficiency projects, clients see a positive ROI within 6 to 12 months. For
projects focused on revenue generation or competitive advantage, the impact is often felt immediately upon
launch, with the full financial ROI realized over a longer term.
Do we need to have our own AI experts to work with you?
No. Our 'Ecosystem of Experts' model is designed to be your complete, outsourced AI team. We handle
everything from strategy and data science to MLOps and maintenance. We integrate with your existing
product and engineering teams, but you are not required to have AI specialists on staff.
What happens after the project is launched?
We offer several options. We can provide ongoing management and optimization through a support retainer,
train your team to take over maintenance, or transition to a less intensive monitoring role. We ensure a
smooth handover and that you are never left with a system you can't manage.
How do you handle intellectual property (IP) rights for the AI models you build?
Your innovation is your competitive advantage. Upon final payment, you receive 100% of the
intellectual property, source code, and model weights. We build it for you, but you own it
entirely. We provide full white-label services to ensure you have complete control over your proprietary
assets.
Can you integrate with our existing legacy systems and databases?
Yes. We specialize in bridging the gap between legacy enterprise systems (ERP, CRM, SQL Databases) and
modern AI. We develop secure middleware, API connectors, and ETL pipelines that allow your existing
infrastructure to feed into and benefit from generative AI without requiring a full system replacement.
What is your approach to AI ethics and bias mitigation?
We govern AI with a proactive, not reactive, mindset. We implement a governance framework based on the
NIST AI Risk Management Framework. This includes automated bias detection during
training, human-in-the-loop review workflows for sensitive applications, and rigorous transparency logs to
ensure your AI remains fair and accountable.
How do you ensure the AI models don't "hallucinate" or provide incorrect
information?
We use Retrieval-Augmented Generation (RAG) to ground the model. Instead of relying on
the model's internal memory, we force it to look up answers in your verified, proprietary knowledge base
first. We also implement strict content filtering and citation requirements, so the AI always provides
evidence for its claims.
What is the difference between a generic AI chatbot and the solutions you build?
Generic tools are commoditized; everyone has access to the same baseline capability. We build
custom, domain-specific AI. By fine-tuning models on your unique data and integrating
them into your specific workflows, we create a solution that understands your industry jargon, your
customer nuances, and your business goals—creating a defensible moat that generic tools cannot match.
Can you help us with AI compliance and regulatory requirements (GDPR, CCPA,
etc.)?
Absolutely. Navigating the regulatory landscape is a core part of our service. We build compliance into
the architecture of your solution from day one, ensuring data anonymization, consent management, and
auditability. We align our delivery process with international standards, helping you minimize legal risk
while maximizing innovation.
Do you provide ongoing training for our internal team after deployment?
Yes. We believe in knowledge transfer. As part of our handover, we provide comprehensive training
sessions for your technical team on how to manage, monitor, and update the AI systems. We also provide
documentation and operational handbooks to ensure your team feels confident managing the solution
long-term.
How do you handle model retraining and updates as new AI research emerges?
The AI field moves fast, and we keep you ahead of the curve. Our MLOps framework includes
continuous performance monitoring. We establish automated pipelines for retraining your
models on new data, and our team regularly reviews your architecture to recommend strategic updates or
model swaps (e.g., migrating to a newer, more efficient base model) to maintain peak performance.
